A Corpus-Based Comparative Study of Chinese and Western Media Image Construction of TCM-Related Personnel
Bibliographic record
Abstract
This study aims to explore the differences in image of personnel related to traditional Chinese medicine (TCM) between Chinese and Western mainstream media. Employing Fairclough’s three-dimensional critical discourse analysis (CDA) model and TCM Social Image Evaluation Index System, it analyzes the linguistic characteristics of high-frequency words and collocation with corpus methods. The findings reveal Chinese media’s emphasis on the management role and Western media’s focus on the alternative or marginalized status of TCM-related personnel. Chinese media portray TCM medical personnel as experienced and professionally excellent, while Western media tend to question their reliability, positioning them as supplementary to mainstream medicine. A certified, professional and reliable image of TCM health care personnel is painted by Chinese media, as opposed to the illegal practices among acupuncturists highlighted by Western media. Chinese media present TCM experts as integrated and top-notch professionals, whereas Western media may critique their viewpoints. TCM administrators are characterized by precision and authority in Chinese reports, contrasting with the negative depiction in Western media. Last but not least, Chinese patients are generally trustful to TCM more than their Western counterparts. The above differences are deeply rooted in the ideological stances of the media outlets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".